Effect of the composition of Au<inf>x</inf>Ag<inf>(1&#x2212;x)</inf> nanoalloys on their nonlinear optical response
Bibliographic record
Abstract
In the present work the nonlinear optical response of some Au <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> Ag <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1-x)</sub> alloy nanoparticles (NPs) prepared by a femtosecond laser ablation process is studied using Z-scan technique employing 4 ns, 532 nm laser pulses. The prepared NPs with diameters of 15 - 20 nm, had different compositions, i.e. gold molar fractions (GMF) x, ranging from x = 0 (pure Ag) to x = 1 (pure Au) and they were exhibiting surface plasmon resonance (SPR) peak whose spectral position and strength were varying depending on GMF. The Au <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> Ag <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1-x)</sub> NPs were found to exhibit negative nonlinear refraction, corresponding to self-defocusing behavior and negligible nonlinear absorption. The nonlinear optical response of the Au <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> Ag <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">(1-x)</sub> alloy nanoparticles was found to be strongly dependent on the GMF of the nanoalloy, since the surface plasmon resonance peak enhances the nonlinear optical response of the metallic nanoalloys when it is close to the excitation wavelength.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".